Using Network Partitioning to Design a Green Supply Chain

Abstract

Abstract In supply chain network design, a retailer may determine the number and locations of facilities based on the cost of opening the facility, a customer driving to the facility, and a replenishment truck driving to the facility from a warehouse. However, this does not include the system’s greenhouse gas (GHG) emissions. Given the existential threat posed by global warming, it is pertinent to consider how the design of the system affects its GHG emissions. We model the supply chain as a network of customers and store locations, with customers driving in cars to and from stores and the retailer resupplying the stores from a central warehouse. The number and location of stores is determined while minimizing the GHG emission. Our contributions are (1) to remove the assumption of uniform demand, and instead build a model of a GSC based on population data; (2) to model the GSC as a two-echelon k-median problem. We conduct a sensitivity analysis to study the effect of a carbon tax in encouraging a greener system considering various scenarios under which emissions might increase or decrease. Specific scenarios lead to a lower overall GHG emission. For example, doubling the fuel efficiency of cars decreases emissions by 46% compared to the baseline scenario. The proposed design approach is not limited to GSC design and can be extended to many design problems, including manufacturing, material design, and healthcare.